A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling

TL;DR

This paper introduces an extended generative Restricted Boltzmann Machine that models both global dynamics and local spatial interactions in high-dimensional motion data, improving classification in vision tasks.

Contribution

The paper proposes a novel RBM extension with an additional energy term for local spatial interactions and a new classification method estimating partition functions.

Findings

Effective in facial expression recognition

Improves human action recognition accuracy

Demonstrates superiority on benchmark datasets

Abstract

Many computer vision applications involve modeling complex spatio-temporal patterns in high-dimensional motion data. Recently, restricted Boltzmann machines (RBMs) have been widely used to capture and represent spatial patterns in a single image or temporal patterns in several time slices. To model global dynamics and local spatial interactions, we propose to theoretically extend the conventional RBMs by introducing another term in the energy function to explicitly model the local spatial interactions in the input data. A learning method is then proposed to perform efficient learning for the proposed model. We further introduce a new method for multi-class classification that can effectively estimate the infeasible partition functions of different RBMs such that RBM is treated as a generative model for classification purpose. The improved RBM model is evaluated on two computer vision applications: facial expression recognition and human action recognition. Experimental results on benchmark databases demonstrate the effectiveness of the proposed algorithm.